Scale-Variant Robust Kernel Optimization for Non-linear Least Squares Problems

In this article, we consider the benefit of increasing adaptivity of an existing robust estimation algorithm by learning two parameters to better fit the residual distribution. Our method uses these two parameters to calculate weights for Iterative Re-weighted Least Squares (IRLS). This adaptive nature of the weights can be helpful in situations where the noise level varies in the measurements. We test our algorithm first on the point cloud registration problem with synthetic data sets and lidar odometry with open-source real-world data sets. We show that the existing approach needs an additional manual tuning of a residual scale parameter which our method directly learns from data and has similar or better performance.